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Estimating functional brain networks by incorporating amodularity prior

Authors
Qiao, LishanZhang, HanKim, MinjeongTeng, ShenghuaZhang, LimeiShen, Dinggang
Issue Date
11월-2016
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Keywords
Brain network; Functional magnetic resonance imaging (fMRI); Pearson' s correlation; Partial correlation; Sparse representation; Modularity; Low-rank representation; Mild cognitive impairment (MCI); Classification
Citation
NEUROIMAGE, v.141, pp.399 - 407
Indexed
SCIE
SCOPUS
Journal Title
NEUROIMAGE
Volume
141
Start Page
399
End Page
407
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/86982
DOI
10.1016/j.neuroimage.2016.07.058
ISSN
1053-8119
Abstract
Functional brain network analysis has become one principled way of revealing informative organization architectures in healthy brains, and providing sensitive biomarkers for diagnosis of neurological disorders. Prior to any post hoc analysis, however, a natural issue is how to construct "ideal" brain networks given, for example, a set of functional magnetic resonance imaging (fMRI) time series associated with different brain regions. Although many methods have been developed, it is currently still an open field to estimate biologically meaningful and statistically robust brain networks due to our limited understanding of the human brain as well as complex noises in the observed data. Motivated by the fact that the brain is organized with modular structures, in this paper, we propose a novel functional brain network modeling scheme by encoding a modularity prior under a matrix-regularized network learning framework, and further formulate it as a sparse low-rank graph learning problem, which can be solved by an efficient optimization algorithm. Then, we apply the learned brain networks to identify patients with mild cognitive impairment (MCI) from normal controls. We achieved 89.01% classification accuracy even with a simple feature selection and classification pipeline, which significantly outperforms the conventional brain network construction methods. Moreover, we further explore brain network features that contributed to MCI identification, and discovered potential biomarkers for personalized diagnosis. (C) 2016 Elsevier Inc. All rights reserved.
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